Personalized brain health management method and system based on big data analysis

Abstract

The invention discloses a personalized brain health management method and system based on big data analysis. The method comprises the following steps: acquiring brain function imaging information; performing identification processing on the brain function imaging information to obtain target image identification result information; and performing big data analysis processing on the target image recognition result information to obtain target analysis result information.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular, to a personalized brain health management method and system based on big data analysis. Background Art

[0002] In the field of brain health management, traditional methods mostly rely on doctors' experience and limited examination means, and it is difficult to meet the needs of individual differences and dynamic changes. In recent years, with the development of brain function imaging technologies, such as functional near-infrared spectroscopy imaging (fNIRS), it is possible to monitor the changes in cerebral blood oxygen and blood flow in real time, providing more accurate physiological indicators for brain health assessment. However, there are still some deficiencies in the practical application of these technologies. On the one hand, the complexity of data collection and analysis makes it difficult for ordinary users to operate and understand by themselves, restricting their popularization in daily health management. On the other hand, although rich data can be obtained, there are lack of effective integration and analysis means, and the potential in personalized health management cannot be fully exerted. At the same time, the rise of big data technology provides a new idea for solving the above problems. By building a big data platform, a large amount of brain health-related data can be collected, stored and analyzed, including multi-dimensional information such as individual physiological data, living habits, and environmental factors. Using technologies such as machine learning and data mining, valuable knowledge can be extracted from the massive data, and a personalized health management model can be established. This can not only improve the accuracy of brain health assessment, but also dynamically adjust the intervention plan according to the individual's real-time data, realizing precise personalized health intervention. Therefore, there is provided a personalized brain health management method and system based on big data analysis to improve the accuracy and efficiency of personalized physical sign data analysis, and further promote the development of brain health management towards the direction of intelligence, personalization and high efficiency, providing more accurate and convenient brain health management services for users. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a personalized brain health management method and system based on big data analysis, which is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, and further promoting the development of brain health management towards the direction of intelligence, personalization and high efficiency, providing more accurate and convenient brain health management services for users.

[0004] To solve the above technical problem, in the first aspect of the embodiments of the present invention, a data analysis method is disclosed, and the method includes: Obtaining brain function imaging map information; Performing recognition processing on the brain function imaging map information to obtain target image recognition result information; Performing big data analysis processing on the target image recognition result information to obtain target analysis result information.

[0005] In a second aspect of the embodiments of the present invention, a data analysis system is disclosed, the system comprising: An acquisition module, configured to acquire brain functional imaging map information; A first processing module, configured to perform recognition processing on the brain functional imaging map information to obtain target image recognition result information; A second processing module, configured to perform big data analysis processing on the target image recognition result information to obtain target analysis result information.

[0006] In a third aspect of the present invention, another data analysis system is disclosed, the system comprising: A memory storing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory and executes some or all of the steps in the data analysis method disclosed in the first aspect of the embodiments of the present invention.

[0007] In a fourth aspect of the present invention, a computer-readable storage medium is disclosed, the computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute some or all of the steps in the data analysis method disclosed in the first aspect of the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0009] Figure 1 is a schematic diagram of the scenario of the data analysis system provided by the embodiments of the present invention; Figure 2 is a schematic flowchart of a data analysis method disclosed in the embodiments of the present invention; Figure 3 is a schematic structural diagram of a data analysis system disclosed in the embodiments of the present invention; Figure 4 is a schematic structural diagram of another data analysis system disclosed in the embodiments of the present invention; Figure 5 is a schematic structural diagram of a target image recognition model disclosed in the embodiments of the present invention; Figure 6 is a schematic structural diagram of a second feature extraction module disclosed in the embodiments of the present invention; Figure 7 is a schematic structural diagram of a third feature extraction module disclosed in the embodiments of the present invention; Figure 8 It is a schematic structural diagram of a feature fusion module disclosed in an embodiment of the present invention. Detailed implementation manners

[0010] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0011] The terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0012] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0013] In this application, the term "exemplary" is used to mean "serving as an example, illustration, or description". Any embodiment described as "exemplary" in this application is not necessarily to be construed as more preferred or more advantageous than other embodiments. In order for any person skilled in the art to implement and use this application, the following description is given. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that this application can be implemented without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid unnecessary details from obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed in this application.

[0014] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time, which is actually time information. It can be understood that in subsequent embodiments, if dimensions, quantities, positions, etc. are mentioned, they are all corresponding data existences for the computer device to process. Details are not elaborated here.

[0015] It should be noted that a brief introduction to the artificial intelligence-related technologies that may be involved in the present application is given. Artificial Intelligence (AI) is to use a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, and is a theory, method, technology, and application system that can perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a way similar to human intelligence. Artificial intelligence also studies the design principles and implementation methods of various intelligent machines, enabling the machines to have the functions of perception, reasoning, and decision-making.

[0016] Artificial intelligence technology is an interdisciplinary subject, involving a wide range of fields, including both hardware-level technologies and software-level technologies. The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0017] Computer Vision Technology (CV) Computer vision is a science that studies how to enable machines to "see". Further, it refers to using cameras and computers to replace human eyes to identify and measure targets in machine vision, and further perform graphic processing to make the computer-processed images more suitable for human eyes to observe or be transmitted to instruments for detection. As a scientific discipline, computer vision studies related theories and technologies and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. Computer vision technology usually includes technologies such as image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, etc., and also includes common biometric recognition technologies such as face recognition and fingerprint recognition.

[0018] Single-modal information is data of only one type, such as one of the data information types like text, image, audio, video, electromagnetic signal, etc. Multi-modal information is data information that includes at least two types of single-modal information. Further, multi-modal information is applicable to complex tasks that require integrating multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information of multiple modalities, higher performance and accuracy can usually be achieved in tasks.

[0019] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model usually refers to a model with hundreds of millions to trillions of parameters. The model usually needs to be trained on a large-scale dataset and requires a large amount of computing resources for optimization and adjustment. Large models are usually used to solve complex natural language processing, computer vision, speech recognition and other tasks. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos and music. In the embodiments of this application, the large model can be large-scale pre-trained models such as ChatGPT series, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Tongwen Qianyi model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Lark model, vivoLM model, deepseek, Tencent Yuanbao and Wenxin Yiyan, and the embodiments of this application do not make limitations.

[0020] The embodiments of this application provide a data analysis method, system, computer device and computer-readable storage medium, which will be described in detail below respectively.

[0021] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the scenario of the data analysis system provided by the embodiments of this application. The data analysis system can include a computer device 100, and the data analysis system is integrated in the computer device 100, such as Figure 1 the computer device in

[0022] In the embodiments of this application, the computer device 100 is mainly used to obtain brain functional imaging map information; perform identification processing on the brain functional imaging map information to obtain target image recognition result information; perform big data analysis processing on the target image recognition result information to obtain target analysis result information.

[0023] It can improve the accuracy and efficiency of personalized physical sign data analysis, and then promote the development of brain health management towards the direction of intelligence, personalization and high efficiency, and provide more accurate and convenient brain health management services for users.

[0024] In the embodiments of the present application, the computer device 100 may be an independent server or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. Among them, the cloud server is composed of a large number of computers or network servers based on cloud computing.

[0025] It can be understood that the computer device 100 used in the embodiments of the present application may be a device that includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such devices may include: cellular or other communication devices, which have a single-line display or a multi-line display or cellular or other communication devices without a multi-line display. Specifically, the computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0026] Those skilled in the art can understand that Figure 1 the application environment shown is only one application scenario of the solution of the present application, and does not constitute a limitation on the application scenario of the solution of the present application. Other application environments may also include more or fewer computer devices than Figure 1 shown. For example Figure 1 only 1 computer device is shown in . It can be understood that the data analysis system may also include one or more other services, which are not specifically limited here.

[0027] In addition, as Figure 1 shown, the data analysis system may also include a memory 200 for storing data, such as image data, location information, etc.

[0028] It should be noted that Figure 1 the schematic diagram of the data analysis system shown is only an example. The data analysis system and scenario described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the evolution of the data analysis system and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0029] The present invention discloses a personalized brain health management method and system based on big data analysis, which is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, and further promoting the development of brain health management towards the direction of intelligence, personalization and high efficiency, and providing more accurate and convenient brain health management services for users. The following will be described in detail respectively.

[0030] Example 1 Please refer to Figure 2 , Figure 2 which is a schematic flowchart of a data analysis method disclosed in an embodiment of the present invention. Among them, Figure 2 the described data analysis method is applied to a management system, such as a local server or a cloud server for management, etc., and the embodiments of the present invention do not make limitations. As Figure 2 shown, the data analysis method may include the following operations: 101. Obtain brain functional imaging map information.

[0031] 102. Perform identification processing on the brain functional imaging map information to obtain target image recognition result information.

[0032] 103. Perform big data analysis processing on the target image recognition result information to obtain target analysis result information.

[0033] It should be noted that the above brain functional imaging map information may be obtained by fNIRS imaging, and the embodiments of the present invention do not make limitations. Further, the above brain functional imaging map information includes several brain functional imaging maps continuously imaged within the first time, and the embodiments of the present invention do not make limitations.

[0034] It should be noted that the above target image recognition result information represents several recognition results of physical sign features of the brain functional imaging map, and the embodiments of the present invention do not make limitations.

[0035] It should be noted that the above target analysis result information represents a trend analysis result of the physical sign features (such as blood oxygen) corresponding to the brain functional imaging map, which may be obtained by analyzing the target image recognition result information through a large model, and the embodiments of the present invention do not make limitations.

[0036] It can be seen that implementing the data analysis method described in the embodiments of the present invention is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, and further promoting the development of brain health management towards the direction of intelligence, personalization and high efficiency, and providing more accurate and convenient brain health management services for users.

[0037] In an optional embodiment, the above-mentioned performing identification processing on the brain functional imaging map information to obtain target image recognition result information includes: Performing pre-processing on the brain functional imaging map information to obtain target image processing information; Using a target image recognition model to extract features from the target image processing information to obtain target image feature information; Performing identification processing on the target image feature information to obtain target image recognition result information.

[0038] In this alternative embodiment, as an alternative implementation, the preprocessing of the brain functional imaging map information to obtain the target image processing information includes: Perform Gaussian filtering and median filtering on the brain functional imaging map information successively to obtain filtered image information; Normalize the filtered image information to obtain normalized image information; Adjust the image size in the normalized image information to 560×560 to obtain the target image processing information.

[0039] It should be noted that the successive Gaussian filtering and median filtering of the brain functional imaging map information are considered because Gaussian filtering has a good inhibitory effect on Gaussian noise, can effectively reduce the random noise in the image, and make the image smoother. While median filtering has a significant effect on removing impulse noise such as salt-and-pepper noise, and can further improve the image quality and remove the possible remaining salt-and-pepper noise. Gaussian filtering will blur the image edges to a certain extent, but median filtering has good edge-preserving ability and can retain the edge and detail information of the image while removing noise, avoiding the edge blurring problem that may be caused by using only Gaussian filtering. The combined use of the two filtering methods can not only effectively remove different types of noise, but also better retain the important features of the image, thereby improving the overall quality of the brain functional imaging map and providing a clearer and more accurate basis for subsequent image analysis and processing. The embodiments of the present invention are not limited.

[0040] It should be noted that the normalization processing of the filtered image information is to adjust the image data to the numerical range of [0,1] for subsequent processing and analysis.

[0041] It should be noted that the recognition processing of the target image feature information can be implemented based on a CNN model, or based on a ResNet model, or based on a large model. The embodiments of the present invention are not limited.

[0042] It can be seen that implementing the data analysis method described in the embodiments of the present invention is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, and further promoting the development of brain health management towards the direction of intelligence, personalization and high efficiency, and providing more accurate and convenient brain health management services for users.

[0043] In another alternative embodiment, as Figure 5 shown, the target image recognition model includes a first feature extraction module, a second feature extraction module, a third feature extraction module, a feature fusion module and a feature processing module; wherein, The input ends of the first feature extraction module, the second feature extraction module, and the third feature extraction module are all configured to receive the model input of the target image recognition model; the output ends of the first feature extraction module, the second feature extraction module, and the third feature extraction module are all connected to the input end of the feature fusion module; the output end of the second feature extraction module is also connected to the input ends of the second convolutional unit and the fourth convolutional unit of the first feature extraction module; the output end of the feature fusion module is connected to the input end of the feature processing module; the output end of the feature processing module is configured to output the model output of the target image recognition model.

[0044] It should be noted that the above-mentioned first feature extraction module mainly extracts local features in the brain functional imaging map to ensure that there are sufficient key detail features in the feature processing module for accurate segmentation of brain functional regions. The embodiments of the present invention are not limited thereto.

[0045] It should be noted that the above-mentioned second feature extraction module mainly extracts boundary features in the brain functional imaging map to accurately locate the boundaries of brain functional regions, and at the same time can more accurately and efficiently extract the edge band features of brain functional regions. The embodiments of the present invention are not limited thereto.

[0046] It should be noted that the above-mentioned third feature extraction module mainly extracts semantic features in the brain functional imaging map. Through the feature extraction method of narrow channels and deep layers, it can be associated to extract sufficient context feature information. The embodiments of the present invention are not limited thereto.

[0047] It should be noted that since the differences among the three different types of features, namely local, boundary, and semantic features, are significant in the above-mentioned feature fusion module, directly fusing the three types of features is likely to cause an imbalance problem in the features. Therefore, in this application, a feature fusion module is used to perform pairwise feature fusion respectively, and then further deep fusion is performed on the fused features, so as to achieve deep fusion of the three different types of features and form an accurate and rich feature representation. The embodiments of the present invention are not limited thereto.

[0048] It should be noted that the above-mentioned feature processing module can be a multi-branch lightweight network constructed based on Deep Dual-resolution Networks or based on Boundary, Local, and Semantic Network, etc., to segment brain functional regions from the extracted boundary features, local features, and semantic features, and form effective brain contour segmentation features. The embodiments of the present invention are not limited thereto.

[0049] It should be noted that the above-mentioned target image recognition model can achieve a comprehensive and diverse rich feature representation of brain functional regions through the extraction and fusion of local, semantic, and boundary features respectively, so as to achieve more accurate and efficient segmentation processing of brain functional regions in the feature processing module, and can effectively improve the accuracy and robustness of the model. The embodiments of the present invention are not limited thereto.

[0050] It should be noted that the above-mentioned target image recognition model can be implemented based on Python 3.7.10 and above on an NVIDIA GeForce RTX 3090 graphics card. The embodiments of the present invention are not limited thereto.

[0051] It should be noted that when the above-mentioned target image recognition model is trained, the loss function can be a cross-entropy loss function, and the evaluation of the training results can use indicators such as accuracy, precision, and recall. The embodiments of the present invention are not limited thereto.

[0052] It should be noted that the training samples of the above-mentioned target image recognition model can be obtained by taking pictures of brain functional region pictures with a high-definition camera device (such as a camera) and then annotating them. The embodiments of the present invention are not limited thereto.

[0053] It should be noted that the training parameters of the above-mentioned target image recognition model include a sample size per batch of not less than 20, an iteration round greater than or equal to 100, and an initial learning rate of 1×10 -4 , and the embodiments of the present invention are not limited thereto.

[0054] It can be seen that implementing the data analysis method described in the embodiments of the present invention is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, and further promoting the development of brain health management towards the direction of intelligence, personalization, and high efficiency, providing more accurate and convenient brain health management services for users.

[0055] In another optional embodiment, the first feature extraction module includes a first convolutional unit, a second convolutional unit, a third convolutional unit, a fourth convolutional unit, a fifth convolutional unit, a first fusion unit, a second fusion unit, a third fusion unit, a fourth fusion unit, a first normalization unit, a second normalization unit, a first activation unit, and a second activation unit; wherein, The input end of the first convolutional unit is configured to receive the model input of the target image recognition model, and the output end of the first convolutional unit is respectively connected to the input end of the first fusion unit and the input end of the second fusion unit; the input end of the second convolutional unit is connected to the output end of the second feature extraction module; the second convolutional unit, the first normalization unit, and the first activation unit are sequentially connected; the output end of the first activation unit is connected to the input end of the first fusion unit; the output end of the first fusion unit is connected to the input end of the second fusion unit; the output end of the second fusion unit is connected to the input end of the third convolutional unit; the output end of the third convolutional unit is respectively connected to the input end of the third fusion unit and the input end of the fourth fusion unit; the input end of the fourth convolutional unit is connected to the output end of the second feature extraction module; the fourth convolutional unit, the second normalization unit, and the second activation unit are sequentially connected; the output end of the second activation unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is connected to the input end of the fourth fusion unit; the output end of the fourth fusion unit is connected to the input end of the fifth convolutional unit; the output end of the fifth convolutional unit is connected to the input end of the feature fusion unit.

[0056] It should be noted that the convolutional kernels of the above-mentioned first convolutional unit, second convolutional unit, third convolutional unit, fourth convolutional unit, and fifth convolutional unit can be one of 1×1, 3×1, 3×3, 5×1, 5×3, 5×5, 7×5, and 7×7, and the stride is 1 or 2. The embodiments of the present invention do not make any limitations.

[0057] It should be noted that the above-mentioned first fusion unit and third fusion unit are constructed based on element-wise multiplication. The embodiments of the present invention do not make any limitations.

[0058] It should be noted that the above-mentioned second fusion unit and fourth fusion unit are constructed based on element-wise addition. The embodiments of the present invention do not make any limitations.

[0059] It should be noted that the above-mentioned first normalization unit and second normalization unit are constructed based on batch normalization layers. The embodiments of the present invention do not make any limitations.

[0060] It should be noted that the above-mentioned first activation unit and second activation unit are constructed based on the sigmoid activation function. The embodiments of the present invention do not make any limitations.

[0061] It should be noted that the number of feature extraction channels in the above-mentioned first feature extraction module is small, which not only ensures the lightweight of the model, but also, due to the adopted feature extraction method of convolution-normalization-activation-fusion, keeps the resolution of the feature image unchanged, avoids the loss of local detail features of the brain functional imaging map, and thus can extract more abundant detailed local features. The embodiments of the present invention do not make any limitations.

[0062] It can be seen that implementing the data analysis method described in the embodiments of the present invention is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, thereby promoting the development of brain health management towards the direction of intelligence, personalization, and high efficiency, and providing more accurate and convenient brain health management services for users.

[0063] In yet another alternative embodiment, as Figure 6 shown, the second feature extraction module includes a sixth convolutional unit, a seventh convolutional unit, an eighth convolutional unit, a ninth convolutional unit, a tenth convolutional unit, an eleventh convolutional unit, a twelfth convolutional unit, a thirteenth convolutional unit, a fourteenth convolutional unit, a fifteenth convolutional unit, a sixteenth convolutional unit, a seventeenth convolutional unit, an eighteenth convolutional unit, a nineteenth convolutional unit, and a fifth fusion unit; wherein, the input end of the sixth convolutional unit is configured to receive the model input of the target image recognition model, and the output end of the sixth convolutional unit is respectively connected to the input end of the second convolutional unit and the input end of the seventh convolutional unit in the first feature extraction module; the output end of the seventh convolutional unit is respectively connected to the input end of the fourth convolutional unit and the input end of the eighth convolutional unit in the first feature extraction module; the output end of the eighth convolutional unit is respectively connected to the input end of the ninth convolutional unit, the input end of the eleventh convolutional unit, the input end of the thirteenth convolutional unit, the input end of the fifteenth convolutional unit, the input end of the seventeenth convolutional unit, and the input end of the nineteenth convolutional unit; the output end of the ninth convolutional unit is connected to the input end of the tenth convolutional unit; the output end of the eleventh convolutional unit is connected to the input end of the twelfth convolutional unit; the output end of the thirteenth convolutional unit is connected to the input end of the fourteenth convolutional unit; the output end of the fifteenth convolutional unit is connected to the input end of the sixteenth convolutional unit; the output end of the seventeenth convolutional unit is connected to the input end of the eighteenth convolutional unit; the output ends of the tenth convolutional unit, the twelfth convolutional unit, the fourteenth convolutional unit, the sixteenth convolutional unit, the eighteenth convolutional unit, and the nineteenth convolutional unit are all connected to the input end of the fifth fusion unit; the output end of the fifth fusion unit is connected to the input end of the feature fusion module.

[0064] It should be noted that the above-mentioned sixth convolutional unit, seventh convolutional unit, eighth convolutional unit, ninth convolutional unit, tenth convolutional unit, eleventh convolutional unit, twelfth convolutional unit, thirteenth convolutional unit, fourteenth convolutional unit, fifteenth convolutional unit, sixteenth convolutional unit, seventeenth convolutional unit, and eighteenth convolutional unit are strip convolutional layers, and the convolutional kernel size is one of 1×3, 1×7, 1×11, 1×15, 1×21, 3×1, 7×1, 11×1, 15×1, and 21×1, and the stride is 1, which is not limited in the embodiments of the present invention.

[0065] It should be noted that the convolution kernel size of the above-mentioned nineteenth convolution unit is 1×1, and the stride is 1, which is not limited in the embodiments of the present invention.

[0066] It should be noted that the above-mentioned fifth fusion unit is constructed based on element-wise addition operation, which is not limited in the embodiments of the present invention.

[0067] It should be noted that the above-mentioned second feature extraction module can achieve multi-scale feature extraction through parallel feature extraction designed with 5 parallel strip convolution feature extraction branches and 1 ordinary convolution branch with a convolution kernel of 1×1, which is more in line with the processing characteristics of brain functional area segmentation. Further, since the execution directions of the 5 parallel strip convolution feature extraction branches are unidirectional, compared with traditional standard convolutions, it can extract long sequence features more effectively on the same computational basis (the pixel distances of the boundary points of brain functional areas vary, and long sequence features are required). Further, through local feature fusion with the first feature extraction module, it can make better use of the local detailed features of local features, thereby forming more effective boundary feature extraction, which is not limited in the embodiments of the present invention.

[0068] It can be seen that implementing the data analysis method described in the embodiments of the present invention is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, and further promotes the development of brain health management towards the direction of intelligence, personalization and high efficiency, providing more accurate and convenient brain health management services for users.

[0069] In an optional embodiment, as above Figure 7 shown, the third feature extraction module includes a first sampling unit, a second sampling unit, a third sampling unit, a twentieth convolution unit, a twenty-first convolution unit, a twenty-second convolution unit, a twenty-third convolution unit, a twenty-fourth convolution unit, and a sixth fusion unit; wherein, The input end of the first sampling unit is configured to receive the model input of the target image recognition model, and the output end of the first sampling unit, the second sampling unit, and the third sampling unit are sequentially connected in sequence; the output end of the third sampling unit is respectively connected to the input ends of the twentieth convolution unit, the twenty-first convolution unit, the twenty-second convolution unit, the twenty-third convolution unit, and the twenty-fourth convolution unit; the output ends of the twentieth convolution unit, the twenty-first convolution unit, the twenty-second convolution unit, the twenty-third convolution unit, and the twenty-fourth convolution unit are all connected to the input end of the sixth fusion unit; the output end of the sixth fusion unit is connected to the input end of the feature fusion module.

[0070] It should be noted that the above-mentioned first sampling unit, second sampling unit, and third sampling unit are constructed based on downsampling operation, which is not limited in the embodiments of the present invention.

[0071] It should be noted that the convolution kernels of the above-mentioned twentieth convolution unit, twenty-first convolution unit, twenty-second convolution unit, twenty-third convolution unit, and twenty-fourth convolution unit can be one of 1×1, 3×1, 3×3, 5×1, 5×3, 5×5, 7×5, and 7×7, and the stride is 1 or 2. The embodiments of the present invention do not make any limitations.

[0072] It should be noted that the above-mentioned sixth fusion unit is constructed based on element-wise addition. The embodiments of the present invention do not make any limitations.

[0073] It should be noted that the above-mentioned continuous three downsampling operations can be used to improve the receptive field of the model, and then five parallel convolution feature extraction units are used to improve the effectiveness of the model's semantic feature extraction. The embodiments of the present invention do not make any limitations.

[0074] It can be seen that implementing the data analysis method described in the embodiments of the present invention is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, thereby promoting the development of brain health management towards the direction of intelligence, personalization, and high efficiency, and providing more accurate and convenient brain health management services for users.

[0075] In another optional embodiment, as Figure 8 shown, the feature fusion module includes a seventh fusion unit, an eighth fusion unit, a ninth fusion unit, a twenty-fifth convolution unit, a third normalization unit, and a third activation unit; among them, The input end of the third activation unit is connected to the output end of the third feature extraction module; the input ends of the seventh fusion unit are respectively connected to the output end of the first feature extraction module and the output end of the third activation unit; the input ends of the eighth fusion unit are respectively connected to the output end of the second feature extraction module and the output end of the third activation unit; the output ends of the seventh fusion unit and the eighth fusion unit are both connected to the input end of the ninth fusion unit; the ninth fusion unit, the twenty-fifth convolution unit, and the third normalization unit are connected in sequence; the output end of the third normalization unit is connected to the input end of the feature processing module.

[0076] It should be noted that the above-mentioned seventh fusion unit and eighth fusion unit are constructed based on element-wise multiplication. The embodiments of the present invention do not make any limitations.

[0077] It should be noted that the above-mentioned ninth fusion unit is constructed based on element-wise addition. The embodiments of the present invention do not make any limitations.

[0078] It should be noted that the convolution kernel size of the above-mentioned twenty-fifth convolution unit is 1×1. The embodiments of the present invention do not make any limitations.

[0079] It should be noted that the above-mentioned third normalization unit is constructed based on a batch normalization layer. The embodiments of the present invention do not make any limitations.

[0080] It should be noted that the above third activation unit is constructed based on the Sigmoid activation function, which is not limited in the embodiments of the present invention.

[0081] It can be seen that implementing the data analysis method described in the embodiments of the present invention is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, and further promoting the development of brain health management towards the direction of intelligence, personalization and high efficiency, so as to provide users with more accurate and convenient brain health management services.

[0082] Embodiment 2 Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a data analysis system disclosed in the embodiments of the present invention. Among them, Figure 3 the described system can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. As Figure 3 shown, the system may include: An acquisition module 201, configured to acquire brain functional imaging map information; A first processing module 202, configured to perform recognition processing on the brain functional imaging map information to obtain target image recognition result information; A second processing module 203, configured to perform big data analysis processing on the target image recognition result information to obtain target analysis result information.

[0083] It can be seen that implementing Figure 3 the described data analysis system is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, and further promoting the development of brain health management towards the direction of intelligence, personalization and high efficiency, so as to provide users with more accurate and convenient brain health management services.

[0084] In another optional embodiment, as Figure 3 shown, performing recognition processing on the brain functional imaging map information to obtain target image recognition result information includes: Performing pre-processing on the brain functional imaging map information to obtain target image processing information; Using a target image recognition model to extract features from the target image processing information to obtain target image feature information; Performing recognition processing on the target image feature information to obtain target image recognition result information.

[0085] It can be seen that implementing Figure 3 the described data analysis system is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, and further promoting the development of brain health management towards the direction of intelligence, personalization and high efficiency, so as to provide users with more accurate and convenient brain health management services.

[0086] In yet another alternative embodiment, as Figure 3 shown, the target image recognition model includes a first feature extraction module, a second feature extraction module, a third feature extraction module, a feature fusion module, and a feature processing module; wherein, The input ends of the first feature extraction module, the second feature extraction module, and the third feature extraction module are all configured to receive the model input of the target image recognition model; the output ends of the first feature extraction module, the second feature extraction module, and the third feature extraction module are all connected to the input end of the feature fusion module; the output end of the second feature extraction module is also connected to the input ends of the second convolutional unit and the fourth convolutional unit of the first feature extraction module; the output end of the feature fusion module is connected to the input end of the feature processing module; the output end of the feature processing module is configured to output the model output of the target image recognition model.

[0087] It can be seen that implementing Figure 3 The described data analysis system is conducive to improving the accuracy and efficiency of personalized physical sign data analysis, and further promoting the development of brain health management towards the direction of intelligence, personalization, and high efficiency, providing more accurate and convenient brain health management services for users.

[0088] In yet another alternative embodiment, as Figure 3 shown, the first feature extraction module includes a first convolutional unit, a second convolutional unit, a third convolutional unit, a fourth convolutional unit, a fifth convolutional unit, a first fusion unit, a second fusion unit, a third fusion unit, a fourth fusion unit, a first normalization unit, a second normalization unit, a first activation unit, and a second activation unit; wherein, The input end of the first convolutional unit is configured to receive the model input of the target image recognition model, and the output end of the first convolutional unit is respectively connected to the input ends of the first fusion unit and the second fusion unit; the input end of the second convolutional unit is connected to the output end of the second feature extraction module; the second convolutional unit, the first normalization unit, and the first activation unit are connected in sequence; the output end of the first activation unit is connected to the input end of the first fusion unit; the output end of the first fusion unit is connected to the input end of the second fusion unit; the output end of the second fusion unit is connected to the input end of the third convolutional unit; the output end of the third convolutional unit is respectively connected to the input ends of the third fusion unit and the fourth fusion unit; the input end of the fourth convolutional unit is connected to the output end of the second feature extraction module; the fourth convolutional unit, the second normalization unit, and the second activation unit are connected in sequence; the output end of the second activation unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is connected to the input end of the fourth fusion unit; the output end of the fourth fusion unit is connected to the input end of the fifth convolutional unit; the output end of the fifth convolutional unit is connected to the input end of the feature fusion unit.

[0089] It can be seen that implementingFigure 3 The described data analysis system is conducive to improving the accuracy and efficiency of personalized physical sign data analysis, thereby promoting the development of brain health management towards the direction of intelligence, personalization and high efficiency, and providing more accurate and convenient brain health management services for users.

[0090] In another alternative embodiment, as Figure 3 shown, the second feature extraction module includes a sixth convolutional unit, a seventh convolutional unit, an eighth convolutional unit, a ninth convolutional unit, a tenth convolutional unit, an eleventh convolutional unit, a twelfth convolutional unit, a thirteenth convolutional unit, a fourteenth convolutional unit, a fifteenth convolutional unit, a sixteenth convolutional unit, a seventeenth convolutional unit, an eighteenth convolutional unit, a nineteenth convolutional unit and a fifth fusion unit; wherein, the input end of the sixth convolutional unit is configured to receive the model input of the target image recognition model, and the output end of the sixth convolutional unit is respectively connected to the input end of the second convolutional unit and the input end of the seventh convolutional unit in the first feature extraction module; the output end of the seventh convolutional unit is respectively connected to the input end of the fourth convolutional unit and the input end of the eighth convolutional unit in the first feature extraction module; the output end of the eighth convolutional unit is respectively connected to the input end of the ninth convolutional unit, the input end of the eleventh convolutional unit, the input end of the thirteenth convolutional unit, the input end of the fifteenth convolutional unit, the input end of the seventeenth convolutional unit and the input end of the nineteenth convolutional unit; the output end of the ninth convolutional unit is connected to the input end of the tenth convolutional unit; the output end of the eleventh convolutional unit is connected to the input end of the twelfth convolutional unit; the output end of the thirteenth convolutional unit is connected to the input end of the fourteenth convolutional unit; the output end of the fifteenth convolutional unit is connected to the input end of the sixteenth convolutional unit; the output end of the seventeenth convolutional unit is connected to the input end of the eighteenth convolutional unit; the output ends of the tenth convolutional unit, the twelfth convolutional unit, the fourteenth convolutional unit, the sixteenth convolutional unit, the eighteenth convolutional unit and the nineteenth convolutional unit are all connected to the input end of the fifth fusion unit; the output end of the fifth fusion unit is connected to the input end of the feature fusion module.

[0091] It can be seen that implementing Figure 3 The described data analysis system is conducive to improving the accuracy and efficiency of personalized physical sign data analysis, thereby promoting the development of brain health management towards the direction of intelligence, personalization and high efficiency, and providing more accurate and convenient brain health management services for users.

[0092] In another alternative embodiment, as Figure 3 shown, the third feature extraction module includes a first sampling unit, a second sampling unit, a third sampling unit, a twentieth convolutional unit, a twenty-first convolutional unit, a twenty-second convolutional unit, a twenty-third convolutional unit, a twenty-fourth convolutional unit and a sixth fusion unit; wherein, The input end of the first sampling unit is configured to receive the model input of the target image recognition model, and the output end of the first sampling unit, the second sampling unit, and the third sampling unit are sequentially connected in series; the output end of the third sampling unit is respectively connected to the input ends of the twentieth convolutional unit, the twenty-first convolutional unit, the twenty-second convolutional unit, the twenty-third convolutional unit, and the twenty-fourth convolutional unit; the output ends of the twentieth convolutional unit, the twenty-first convolutional unit, the twenty-second convolutional unit, the twenty-third convolutional unit, and the twenty-fourth convolutional unit are all connected to the input end of the sixth fusion unit; the output end of the sixth fusion unit is connected to the input end of the feature fusion module.

[0093] It can be seen that implementing Figure 3 the described data analysis system is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, thereby promoting the development of brain health management towards the direction of intelligence, personalization, and high efficiency, and providing more accurate and convenient brain health management services for users.

[0094] In another alternative embodiment, as Figure 3 shown, the feature fusion module includes a seventh fusion unit, an eighth fusion unit, a ninth fusion unit, a twenty-fifth convolutional unit, a third normalization unit, and a third activation unit; wherein, the input end of the third activation unit is connected to the output end of the third feature extraction module; the input ends of the seventh fusion unit are respectively connected to the output end of the first feature extraction module and the output end of the third activation unit; the input ends of the eighth fusion unit are respectively connected to the output end of the second feature extraction module and the output end of the third activation unit; the output ends of the seventh fusion unit and the eighth fusion unit are both connected to the input end of the ninth fusion unit; the ninth fusion unit, the twenty-fifth convolutional unit, and the third normalization unit are sequentially connected in series; the output end of the third normalization unit is connected to the input end of the feature processing module.

[0095] It can be seen that implementing Figure 3 the described data analysis system is beneficial to improving the accuracy and efficiency of personalized physical sign data analysis, thereby promoting the development of brain health management towards the direction of intelligence, personalization, and high efficiency, and providing more accurate and convenient brain health management services for users.

[0096] Embodiment III Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of another data analysis system disclosed in the embodiments of the present invention. Among them, Figure 4 the described system can be applied to a management system, such as a local server or a cloud server for management, etc., and the embodiments of the present invention do not make any limitations. As Figure 4 shown, the system may include: A memory 301 storing executable program code; A processor 302 coupled to the memory 301; The processor 302 invokes the executable program code stored in the memory 301 to execute the steps in the data analysis method described in the first embodiment.

[0097] Embodiment Four An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps in the data analysis method described in the first embodiment.

[0098] Embodiment Five An embodiment of the present invention discloses a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps in the data analysis method described in the first embodiment.

[0099] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative effort.

[0100] Through the specific descriptions of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, and the storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0101] Finally, it should be noted that: The personalized brain health management method and system disclosed in the embodiments of the present invention are only the preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data analysis method, characterized in that, The method includes: Obtaining brain functional imaging map information; Performing recognition processing on the brain functional imaging map information to obtain target image recognition result information; Performing big data analysis processing on the target image recognition result information to obtain target analysis result information.

2. The data analysis method according to claim 1, wherein The performing recognition processing on the brain functional imaging map information to obtain target image recognition result information includes: Performing preprocessing on the brain functional imaging map information to obtain target image processing information; Using a target image recognition model to extract features from the target image processing information to obtain target image feature information; Performing recognition processing on the target image feature information to obtain target image recognition result information.

3. The data analysis method according to claim 2, wherein The target image recognition model includes a first feature extraction module, a second feature extraction module, a third feature extraction module, a feature fusion module, and a feature processing module; wherein, The input ends of the first feature extraction module, the second feature extraction module, and the third feature extraction module are all configured to receive the model input of the target image recognition model; the output ends of the first feature extraction module, the second feature extraction module, and the third feature extraction module are all connected to the input end of the feature fusion module; the output end of the second feature extraction module is further connected to the input ends of the second convolutional unit and the fourth convolutional unit of the first feature extraction module; the output end of the feature fusion module is connected to the input end of the feature processing module; the output end of the feature processing module is configured to output the model output of the target image recognition model.

4. The data analysis method according to claim 3, wherein The first feature extraction module includes a first convolutional unit, a second convolutional unit, a third convolutional unit, a fourth convolutional unit, a fifth convolutional unit, a first fusion unit, a second fusion unit, a third fusion unit, a fourth fusion unit, a first normalization unit, a second normalization unit, a first activation unit, and a second activation unit; wherein, The input end of the first convolutional unit is configured to receive the model input of the target image recognition model, and the output end of the first convolutional unit is respectively connected to the input end of the first fusion unit and the input end of the second fusion unit; the input end of the second convolutional unit is connected to the output end of the second feature extraction module; the second convolutional unit, the first normalization unit, and the first activation unit are sequentially connected; the output end of the first activation unit is connected to the input end of the first fusion unit; the output end of the first fusion unit is connected to the input end of the second fusion unit; the output end of the second fusion unit is connected to the input end of the third convolutional unit; the output end of the third convolutional unit is respectively connected to the input end of the third fusion unit and the input end of the fourth fusion unit; the input end of the fourth convolutional unit is connected to the output end of the second feature extraction module; the fourth convolutional unit, the second normalization unit, and the second activation unit are sequentially connected; the output end of the second activation unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is connected to the input end of the fourth fusion unit; the output end of the fourth fusion unit is connected to the input end of the fifth convolutional unit; the output end of the fifth convolutional unit is connected to the input end of the feature fusion unit.

5. The data analysis method according to claim 3, wherein The second feature extraction module includes a sixth convolutional unit, a seventh convolutional unit, an eighth convolutional unit, a ninth convolutional unit, a tenth convolutional unit, an eleventh convolutional unit, a twelfth convolutional unit, a thirteenth convolutional unit, a fourteenth convolutional unit, a fifteenth convolutional unit, a sixteenth convolutional unit, a seventeenth convolutional unit, an eighteenth convolutional unit, a nineteenth convolutional unit, and a fifth fusion unit; where The input end of the sixth convolution unit is configured to receive the model input of the target image recognition model. The output end of the sixth convolution unit is respectively connected to the input end of the second convolution unit in the first feature extraction module and the input end of the seventh convolution unit. The output end of the seventh convolution unit is respectively connected to the input end of the fourth convolution unit in the first feature extraction module and the input end of the eighth convolution unit. The output end of the eighth convolution unit is respectively connected to the input end of the ninth convolution unit, the input end of the eleventh convolution unit, the input end of the thirteenth convolution unit, the input end of the fifteenth convolution unit, the input end of the seventeenth convolution unit, and the input end of the nineteenth convolution unit. The output end of the ninth convolution unit is connected to the input end of the tenth convolution unit. The output end of the eleventh convolution unit is connected to the input end of the twelfth convolution unit. The output end of the thirteenth convolution unit is connected to the input end of the fourteenth convolution unit. The output end of the fifteenth convolution unit is connected to the input end of the sixteenth convolution unit. The output end of the seventeenth convolution unit is connected to the input end of the eighteenth convolution unit. The output ends of the tenth convolution unit, the twelfth convolution unit, the fourteenth convolution unit, the sixteenth convolution unit, the eighteenth convolution unit, and the nineteenth convolution unit are all connected to the input end of the fifth fusion unit. The output end of the fifth fusion unit is connected to the input end of the feature fusion module.

6. The data analysis method according to claim 3, wherein The third feature extraction module includes a first sampling unit, a second sampling unit, a third sampling unit, a twentieth convolution unit, a twenty-first convolution unit, a twenty-second convolution unit, a twenty-third convolution unit, a twenty-fourth convolution unit, and a sixth fusion unit. Among them, The input end of the first sampling unit is configured to receive the model input of the target image recognition model. The output end of the first sampling unit, the second sampling unit, and the third sampling unit are connected in sequence. The output end of the third sampling unit is respectively connected to the input end of the twentieth convolution unit, the input end of the twenty-first convolution unit, the input end of the twenty-second convolution unit, the input end of the twenty-third convolution unit, and the input end of the twenty-fourth convolution unit. The output ends of the twentieth convolution unit, the twenty-first convolution unit, the twenty-second convolution unit, the twenty-third convolution unit, and the twenty-fourth convolution unit are all connected to the input end of the sixth fusion unit. The output end of the sixth fusion unit is connected to the input end of the feature fusion module.

7. The data analysis method according to claim 3, wherein The feature fusion module includes a seventh fusion unit, an eighth fusion unit, a ninth fusion unit, a twenty-fifth convolution unit, a third normalization unit, and a third activation unit. Among them, The input end of the third activation unit is connected to the output end of the third feature extraction module; the input ends of the seventh fusion unit are respectively connected to the output end of the first feature extraction module and the output end of the third activation unit; the input ends of the eighth fusion unit are respectively connected to the output end of the second feature extraction module and the output end of the third activation unit; the output ends of the seventh fusion unit and the eighth fusion unit are both connected to the input end of the ninth fusion unit; the ninth fusion unit, the twenty-fifth convolutional unit and the third normalization unit are connected in sequence; the output end of the third normalization unit is connected to the input end of the feature processing module.

8. A data analysis system, characterized in that, The system includes: An acquisition module, configured to acquire brain functional imaging map information; A first processing module, configured to perform recognition processing on the brain functional imaging map information to obtain target image recognition result information; A second processing module, configured to perform big data analysis processing on the target image recognition result information to obtain target analysis result information.

9. A data analysis system, characterized in that, The system includes: A memory storing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory and executes the data analysis method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which are used to execute the data analysis method according to any one of claims 1-7 when the computer instructions are called.

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